Generally, the present invention relates to computerized systems and methods for analyzing hospital electronic discharge data to accurately predict the resources required to treat a patient. Further, the present invention relates to computerized systems and methods applied to such electronic discharge data to allow statistical patient classification based upon certain key explanatory variables of a patient's condition.
In the age of managed health care and government medical care programs (e.g., Medicare in the US), it has become increasingly important for hospitals or other patient care institutions to accurately monitor their costs and justify their treatment procedures. Hospitals contract with HMO's (health maintenance organizations) and other managed care providers to provide services for the patients enrolled by the HMO. HMOs contract with employers to enroll employees in their health care plans.
If two hospitals are vying for a contract from the same HMO, the hospital having a lower cost per patient should normally win such contract--all other features being equal. However, the cost differential may not necessarily be due to inefficiencies in the higher cost hospital. It may simply be that the higher cost hospital is treating, on average, sicker patients or patients requiring more care. If the managed health care provider can be made to understand this, it may actually decide to contract with the higher cost hospital. Similarly, when a managed health care provider is contracting with an employer, the health care organization may wish to charge some employers more than others to enroll their employees. This can be justified if the employer has a class of employees that are, on average, likely to be sicker or require more care than the employees of some other employer. Obviously, the health care provider must convince the employer that it is charging a higher premium for that employer's employees because they are actually more of a high cost risk. Unfortunately, no precise mechanism exists for this purpose--although a measure known as a "DRG" has been applied.
In the mid-1980's, the United States began, through it's Medicare program, to reimburse hospitals and other health care institutions a fixed dollar amount based upon a "diagnosis related group" (DRG) determined from certain medical conditions recorded by the hospital or organization attending to the patient. This practice has spread beyond the government and throughout the industry to HMOs, PPOs, etc. Each DRG classification is comprised of one or more codes (e.g., "ICD-9 codes" currently used in the US as specified in the International Classification of Disease, 9th Revision, Clinical Modification) each of which represents a specific medical condition. For example, ICD-9 code 42.0 represents a patient that is HIV positive, ICD-9 code 789.59 represents a patient which has septic shock, ICD-9 code 410.9 represents a patient who had a myocardial infarction. All told, there are several thousand such ICD-9 codes.
Obviously, a hospital must properly identify the ICD-9 codes for each of its patients. Hospitals now have internal staff groups (medical records librarians) whose sole function is to identify proper ICD-9 codes and input them into patient records. To do this, such hospital financial staff groups take the clinical records provided by the physicians and nurses attending to a patient and attempt to determine which ICD-9 codes fit with the clinical records available to them. The hospital financial group then inputs a collection of ICD-9 codes in a standard electronic form (a "UB-92" form in the US). These and similar forms will often be referred to herein generically as hospital or health care "electronic discharge records."
The codes are entered in a specified order (e.g., the first ICD-9 code represents the principal condition for which the patient was treated, and subsequent ICD-9 codes may represent the conditions present at the hospital admission and other conditions that develop during the patient's stay). In the US, the codes are entered pursuant to a protocol specified by The Health Care Financing Agency ("HCFA"). The electronically formatted ICD-9 codes (together with other demographic information) for each patient are then used to classify patients into particular DRGs. A specific protocol governs DRG classification; for example, the principal diagnostic code, the presence of comorbidity conditions, the patient's age, the use of surgical procedures, and death or survival together dictate the DRG classification.
Unfortunately, there is considerable variance in costs for treating all those patients falling within a specific DRG. This is because DRGs do not do a particularly good job of classifying patients with sufficient specificity. For example, a DRG associated with a particular condition may provide reimbursement at a rate of $100,000 per patient. However, within the class of patients meeting this DRG, some may only cost the hospital $50,000 while others may cost the hospital $500,000 or more. Obviously, if one hospital has more than its share of patients falling within the DRG category for the particular condition and yet costing well in excess of the approximately $25,000 average charge, the hospital would like to be able to explain its additional costs.
The problem is exacerbated because the medical records librarians responsible for entering the ICD-9 codes and other information frequently fail to enter those codes that directly specify patient conditions. For example, ICD-9 code 038.9 specifies that a patient has sepsis. However, analysis of a large sample of UB-92 records has shown that only 27 percent of all sepsis patients actually have had code 038.9 recorded. Obviously, it is therefore not possible to identify all patients having sepsis (or most any other serious condition for that matter) by simply analyzing a hospital's electronic discharge records (e.g., the UB-92 records). This problem arises in part because of the large number of codes and the associated complexity of the ICD-9 code system. It also arises in part because the poor linking between a hospital's clinical production system (discharge summaries, doctors and nurses notes, etc.) and its financial billing system (ICD-9 coded records).
To address these and other issues, some "risk adjustment tools" have been developed. In some cases, these attempt to explain the statistical variance in cost of treating the patients falling under a particular DRG (or other generic patient classification). Early risk adjustment tools were simple models used by insurance companies for health insurance underwriting and pricing individual premiums. More sophisticated modern tools were developed by deploying powerful statistical software to analyze patient databases and identify patterns in patient records. These tools were then provided as software to gauge risk within certain patient populations.
All risk adjustment tools model some combination of demographic and/or health status data. See "Risk Adjustment" by C. Lee and D. Rogal, produced for The Robert Wood Johnson Foundation, March 1997. Demographic models generally include some or all of the following variables: age, sex, family status, geographic location, and welfare status. Measures of health status can include survey data of health, diagnoses, and data reflecting prior utilization. Health status models may include demographic variables as predictors of health.
Some more recent risk adjustment tools employ ICD-9 codes to classify patients. See the above-referenced "Risk Adjustment" paper, pages 14-15. Unfortunately, the best of the existing tools typically can explain no more than about 45 percent of the variance in patient cost within a given DRG. This leaves over 50 percent of the variance unexplained. In addition, known risk adjustment tools were developed using fairly narrow databases, and therefore predict best for a specific population group or health care setting.
In view of the above, medical records analysis technology could be improved to provide a method for ascribing the variance within a generic class of patients (such as all patients grouped into the DRG for particular patient condition category).